AbstractThe metabolically connected triad of obesity, diabetes, and cardiovascular diseases is a major public health threat, and is expected to worsen due to the global shift toward energy-rich and sedentary living. Despite decades of intense research, a large part of the molecular pathogenesis behind complex metabolic diseases remains unknown. Recent advances in genetics, epigenomics, transcriptomics, proteomics and metabolomics enable us to obtain large-scale snapshots of the etiological processes in multiple disease-related cells, tissues and organs. These datasets provide us with an opportunity to go beyond conventional reductionist approaches and to pinpoint the specific perturbations in critical biological processes. In this review, we summarize systems biology methodologies such as functional genomics, causality inference, data-driven biological network construction, and higher-level integrative analyses that can produce novel mechanistic insights, identify disease biomarkers, and uncover potential therapeutic targets from a combination of omics datasets. Importantly, we also demonstrate the power of these approaches by application examples in obesity, diabetes, and cardiovascular diseases.

The article is published online in Current Cardiovascular Risk Reports and is free to access.

RELATED ARTICLES

Can artificial intelligence (AI) help us understand how the brain understands language? Can neuroscience help us understand why AI and neural networks are effective at predicting human perception? New research suggests both are possible.

y conjuring the spell “Lumos!” wizards in the mythical world of Harry Potter could light up the tip of their magic wands and illuminate their surroundings. So, too, does LumosVar, a computer program that “lights up” cancer-causing genetic Var-ients, or mutations, illuminating how physicians might best treat their patients.

Scientists have developed a way to identify the beginning of every gene — known as a translation start site or a start codon — in bacterial cell DNA with a single experiment and, through this method, they have shown that an individual gene is capable of coding for more than one protein.